CN113449762A - Smart city Internet of things fire-fighting remote monitoring method and system - Google Patents
Smart city Internet of things fire-fighting remote monitoring method and system Download PDFInfo
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- CN113449762A CN113449762A CN202010758752.XA CN202010758752A CN113449762A CN 113449762 A CN113449762 A CN 113449762A CN 202010758752 A CN202010758752 A CN 202010758752A CN 113449762 A CN113449762 A CN 113449762A
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G06F18/20—Analysing
- G06F18/22—Matching criteria, e.g. proximity measures
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- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F16/9035—Filtering based on additional data, e.g. user or group profiles
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Abstract
The embodiment of the invention provides a smart city Internet of things fire-fighting remote monitoring method and system, which respectively extract Internet of things fire-fighting characteristic vectors of first fire-fighting remote monitoring acquisition data and Internet of things fire-fighting characteristic vectors of second fire-fighting remote monitoring acquisition data through a remote monitoring analysis model, respectively take each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculate the vector matching degree of the Internet of things fire-fighting characteristic vectors of the second fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vectors of the first fire-fighting remote monitoring acquisition data, further determine associated multidimensional time-space domain characteristic vectors of the second fire-fighting remote monitoring acquisition data, screen the second fire-fighting remote monitoring acquisition data, and use the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data for model training together, can improve the accuracy of wisdom city thing networking fire control remote monitoring.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a smart city Internet of things fire-fighting remote monitoring method and system.
Background
How to improve the accuracy of wisdom city thing networking fire control remote monitoring is the technical problem that this field needs to be solved urgently.
Disclosure of Invention
In view of this, an object of the embodiments of the present invention is to provide a method and a system for remotely monitoring smart city internet of things fire fighting, which can improve accuracy of remote monitoring of smart city internet of things fire fighting.
According to an aspect of an embodiment of the present invention, there is provided a smart city internet of things fire-fighting remote monitoring method, including:
acquiring a fire-fighting remote monitoring acquisition data sequence; the fire-fighting remote monitoring acquisition data sequence comprises first fire-fighting remote monitoring acquisition data and second fire-fighting remote monitoring acquisition data; the number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one;
respectively extracting the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data through a remote monitoring analysis model; the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning;
respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree of the Internet of things fire-fighting characteristic vector of the target fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vector of each first fire-fighting remote monitoring acquisition data, and taking the vector matching degree as a fire-fighting reference object to obtain the fire-fighting grade characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
determining a relevant multidimensional time-space domain characteristic vector of each second fire-fighting remote monitoring acquisition data according to the vector matching degree of each second fire-fighting remote monitoring acquisition data on the fire-fighting characteristic vector of the Internet of things; or respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, and determining a fire-fighting reference object according to the vector matching degree to obtain a relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
and screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space domain characteristic vector of the second fire-fighting remote monitoring acquisition data, wherein the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
According to another aspect of the application, a smart city internet of things fire-fighting remote monitoring system is provided, the system includes:
the acquisition module is used for acquiring a fire-fighting remote monitoring acquisition data sequence; the fire-fighting remote monitoring acquisition data sequence comprises first fire-fighting remote monitoring acquisition data and second fire-fighting remote monitoring acquisition data; the number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one;
the extraction module is used for respectively extracting the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data through a remote monitoring analysis model; the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning;
the computing module is used for respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, computing the vector matching degree of the Internet of things fire-fighting characteristic vector of the target fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vector of each first fire-fighting remote monitoring acquisition data, and taking the vector matching degree as a fire-fighting reference object to obtain a fire-fighting grade characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
the determining module is used for determining the associated multidimensional time-space domain characteristic vector of each second fire-fighting remote monitoring acquisition data according to the vector matching degree of each second fire-fighting remote monitoring acquisition data on the fire-fighting characteristic vector of the Internet of things; or respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, and determining a fire-fighting reference object according to the vector matching degree to obtain a relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
and the screening module is used for screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space characteristic vector of the second fire-fighting remote monitoring acquisition data, and the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
Compared with the prior art, the smart city Internet of things fire-fighting remote monitoring method and system provided by the embodiment of the invention respectively extract the Internet of things fire-fighting feature vector of each first fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting feature vector of each second fire-fighting remote monitoring acquisition data through the remote monitoring analysis model, respectively take each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculate the vector matching degree of the Internet of things fire-fighting feature vector of each first fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting feature vector of each second fire-fighting remote monitoring acquisition data, further determine the associated multidimensional time-space domain feature vector of each second fire-fighting remote monitoring acquisition data, screen the second fire-fighting remote monitoring acquisition data, and screen the second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data for model training together, can improve the accuracy of wisdom city thing networking fire control remote monitoring.
In order to make the aforementioned objects, features and advantages of the embodiments of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that need to be called in the embodiments are briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention, and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
FIG. 1 illustrates a component diagram of a server provided by an embodiment of the invention;
fig. 2 is a schematic flow chart illustrating a smart city internet of things fire-fighting remote monitoring method according to an embodiment of the present invention;
fig. 3 shows a functional block diagram of a smart city internet of things fire-fighting remote monitoring system according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood by the scholars in the technical field, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It should be understood that the technology project objects so used may be interchanged under appropriate circumstances such that embodiments of the invention described herein may, for example, be implemented in sequences other than those illustrated or described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Fig. 1 shows an exemplary component schematic of a server 100. The server 100 may include one or more processors 104, such as one or more Central Processing Units (CPUs), each of which may implement one or more hardware threads. The server 100 may also include any storage media 106 for storing any kind of information, such as code, settings, data, etc. For example, and without limitation, storage medium 106 may include any one or more of the following in combination: any type of RAM, any type of ROM, flash memory devices, hard disks, optical disks, etc. More generally, any storage medium may use any technology to store information. Further, any storage medium may provide volatile or non-volatile retention of information. Further, any storage medium may represent a fixed or removable component of server 100. In one case, when the processor 104 executes the associated instructions stored in any storage medium or combination of storage media, the server 100 may perform any of the operations of the associated instructions. The server 100 further comprises one or more drive units 108 for interacting with any storage medium, such as a hard disk drive unit, an optical disk drive unit, etc.
The server 100 also includes input/output 110 (I/O) for receiving various inputs (via input unit 112) and for providing various outputs (via output unit 114)). One particular output mechanism may include a presentation device 116 and an associated Graphical User Interface (GUI) 118. The server 100 may also include one or more network interfaces 120 for exchanging data with other devices via one or more communication units 122. One or more communication buses 124 couple the above-described components together.
The communication unit 122 may be implemented in any manner, such as over a local area network, a wide area network (e.g., the internet), a point-to-point connection, etc., or any combination thereof. The communication unit 122 may include any combination of hardwired links, wireless links, routers, gateway functions, name servers 100, and so forth, governed by any protocol or combination of protocols.
Fig. 2 is a schematic flow chart illustrating a smart city internet of things fire-fighting remote monitoring method according to an embodiment of the present invention, which can be executed by the server 100 shown in fig. 1, and the detailed steps of the smart city internet of things fire-fighting remote monitoring method are described as follows.
And step S110, acquiring a fire-fighting remote monitoring acquisition data sequence. The fire-fighting remote monitoring data collection sequence comprises first fire-fighting remote monitoring data collection and second fire-fighting remote monitoring data collection. The number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one.
And S120, respectively extracting the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data through a remote monitoring analysis model. And the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning.
Step S130, each second fire-fighting remote monitoring acquisition data is used as target fire-fighting remote monitoring acquisition data, the vector matching degree of the Internet of things fire-fighting characteristic vector of the target fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vector of each first fire-fighting remote monitoring acquisition data is calculated, the vector matching degree is used as a fire-fighting reference object, and the fire-fighting grade characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data is obtained. The dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring collected data. And the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data.
And S140, determining the associated multidimensional time-space domain characteristic vector of each second fire-fighting remote monitoring and collecting data according to the vector matching degree of each second fire-fighting remote monitoring and collecting data on the fire-fighting characteristic vector of the Internet of things. Or taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, determining a fire-fighting reference object according to the vector matching degree, and obtaining the relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data. And the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data. And the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data.
And S150, screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space domain characteristic vector of the second fire-fighting remote monitoring acquisition data, wherein the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
Based on the steps, the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data are respectively extracted through a remote monitoring analysis model, each second fire fighting remote monitoring acquisition data is respectively used as target fire fighting remote monitoring acquisition data, the vector matching degree of the Internet of things fire fighting characteristic vector and the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data is calculated, and the associated multidimensional time-space domain characteristic vector of each second fire fighting remote monitoring acquisition data is further determined, from this screening second fire control remote monitoring data collection, the second fire control remote monitoring data collection and the first fire control remote monitoring data collection of selecting are used for the model training jointly, can improve wisdom city thing networking fire control remote monitoring's accuracy.
Fig. 3 is a functional block diagram of a smart city internet of things fire-fighting remote monitoring system 200 according to an embodiment of the present invention, where the functions implemented by the smart city internet of things fire-fighting remote monitoring system 200 may correspond to the steps executed by the foregoing method. The smart city internet of things fire-fighting remote monitoring system 200 can be understood as the server 100, or a processor of the server 100, or can be understood as a component which is independent from the server 100 or the processor and realizes the functions of the invention under the control of the server 100, as shown in fig. 3, the functions of each functional module of the smart city internet of things fire-fighting remote monitoring system 200 are explained in detail below.
And the obtaining module 210 is configured to obtain a fire-fighting remote monitoring acquisition data sequence. The fire-fighting remote monitoring data collection sequence comprises first fire-fighting remote monitoring data collection and second fire-fighting remote monitoring data collection. The number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one.
And the extraction module 220 is used for respectively extracting the Internet of things fire fighting characteristic vectors of the first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vectors of the second fire fighting remote monitoring acquisition data through a remote monitoring analysis model. And the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning.
And the calculating module 230 is configured to use each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculate a vector matching degree between the internet of things fire-fighting feature vector of the target fire-fighting remote monitoring acquisition data and the internet of things fire-fighting feature vector of each first fire-fighting remote monitoring acquisition data, and use the vector matching degree as a fire-fighting reference object to obtain the fire-fighting class feature vector corresponding to the target fire-fighting remote monitoring acquisition data. The dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring collected data. And the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data.
And the determining module 240 is configured to determine the associated multidimensional time-space domain feature vector of each second fire-fighting remote monitoring acquired data according to the vector matching degree of each second fire-fighting remote monitoring acquired data on the fire-fighting feature vector of the internet of things. Or taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, determining a fire-fighting reference object according to the vector matching degree, and obtaining the relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data. And the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data. And the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data.
And the screening module 250 is used for screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space domain characteristic vector of the second fire-fighting remote monitoring acquisition data, and the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method can be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, the functional modules in the embodiments of the present invention may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, the computer instructions may be transmitted from one website, computer, server, or data presentation object to another website, computer, server, or data presentation object by wire (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device that includes one or more available media integrated servers, data presentation objects, and the like. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any drawing credit or debit acknowledgement in the claims should not be construed as limiting the claim concerned.
Claims (2)
1. A smart city Internet of things fire-fighting remote monitoring method is characterized by comprising the following steps:
acquiring a fire-fighting remote monitoring acquisition data sequence; the fire-fighting remote monitoring acquisition data sequence comprises first fire-fighting remote monitoring acquisition data and second fire-fighting remote monitoring acquisition data; the number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one;
respectively extracting the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data through a remote monitoring analysis model; the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning;
respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree of the Internet of things fire-fighting characteristic vector of the target fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vector of each first fire-fighting remote monitoring acquisition data, and taking the vector matching degree as a fire-fighting reference object to obtain the fire-fighting grade characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
determining a relevant multidimensional time-space domain characteristic vector of each second fire-fighting remote monitoring acquisition data according to the vector matching degree of each second fire-fighting remote monitoring acquisition data on the fire-fighting characteristic vector of the Internet of things; or respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, and determining a fire-fighting reference object according to the vector matching degree to obtain a relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
and screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space domain characteristic vector of the second fire-fighting remote monitoring acquisition data, wherein the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
2. The utility model provides a wisdom city thing networking fire control remote monitering system which characterized in that, the system includes:
the acquisition module is used for acquiring a fire-fighting remote monitoring acquisition data sequence; the fire-fighting remote monitoring acquisition data sequence comprises first fire-fighting remote monitoring acquisition data and second fire-fighting remote monitoring acquisition data; the number of the first fire-fighting remote monitoring acquisition data and the number of the second fire-fighting remote monitoring acquisition data are more than one;
the extraction module is used for respectively extracting the Internet of things fire fighting characteristic vector of each first fire fighting remote monitoring acquisition data and the Internet of things fire fighting characteristic vector of each second fire fighting remote monitoring acquisition data through a remote monitoring analysis model; the remote monitoring analysis model is obtained according to the first fire-fighting remote monitoring acquisition data through unsupervised learning;
the computing module is used for respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, computing the vector matching degree of the Internet of things fire-fighting characteristic vector of the target fire-fighting remote monitoring acquisition data and the Internet of things fire-fighting characteristic vector of each first fire-fighting remote monitoring acquisition data, and taking the vector matching degree as a fire-fighting reference object to obtain a fire-fighting grade characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the fire-fighting grade characteristic vector is the same as the quantity of the first fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the fire-fighting grade characteristic vector is inversely proportional to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
the determining module is used for determining the associated multidimensional time-space domain characteristic vector of each second fire-fighting remote monitoring acquisition data according to the vector matching degree of each second fire-fighting remote monitoring acquisition data on the fire-fighting characteristic vector of the Internet of things; or respectively taking each second fire-fighting remote monitoring acquisition data as target fire-fighting remote monitoring acquisition data, calculating the vector matching degree between the acquisition continuous characteristic vector of the target fire-fighting remote monitoring acquisition data and the acquisition continuous characteristic vector of each second fire-fighting remote monitoring acquisition data, and determining a fire-fighting reference object according to the vector matching degree to obtain a relevant multi-dimensional time-space domain characteristic vector corresponding to the target fire-fighting remote monitoring acquisition data; the dimension index of the associated multi-dimensional time-space domain feature vector is the same as the quantity of the second fire-fighting remote monitoring acquisition data; the fire-fighting reference object grade of the associated multi-dimensional time-space domain characteristic vector is in direct proportion to the acquisition continuous grade of the second fire-fighting remote monitoring acquisition data;
and the screening module is used for screening the second fire-fighting remote monitoring acquisition data based on the acquisition continuous characteristic vector and the associated multi-dimensional time-space characteristic vector of the second fire-fighting remote monitoring acquisition data, and the screened second fire-fighting remote monitoring acquisition data and the first fire-fighting remote monitoring acquisition data are jointly used for model training.
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| CN202010758752.XA CN113449762A (en) | 2020-07-31 | 2020-07-31 | Smart city Internet of things fire-fighting remote monitoring method and system |
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| CN202010758752.XA CN113449762A (en) | 2020-07-31 | 2020-07-31 | Smart city Internet of things fire-fighting remote monitoring method and system |
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2020
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